Mitra AI
Website:
mitrai.com
Job details:
We are seeking a Data Support Engineer to serve as the key operational link between our production data architecture and our business clients/stakeholders. In this role, you will be responsible for active ETL build monitoring across our dbt Mesh (dbt Core) and Snowflake ecosystem, as well as managing client-facing ticket resolution and incident communication.
You will ensure that scheduled dbt builds run reliably, data freshness SLAs are met, and incoming user inquiries regarding data availability, access, or performance are handled with high client satisfaction.
Key Responsibilities
1. ETL Build Monitoring & Incident Management
- Pipeline Monitoring: Actively monitor nightly and intraday dbt build executions, dbt test assertions, and freshness checks across interconnected dbt Mesh projects.
- Build Failures & Recovery: Diagnose and resolve failed ETL/dbt runs (e.g., upstream source delays, contract breaches, timeout errors, connection drops) to restore pipeline operations within target SLAs.
- Runaway / Blocked Jobs: Track orchestration platform runs (Airflow, Dagster, GitHub Actions, or Snowflake Tasks) to re-trigger failed job steps or clear blocked dependencies.
2. Client-Facing Ticket Management & Communication
- Ticket Lifecycle: Own the queue of client-facing tickets (via Jira, ServiceNow, Zendesk, or Slack/Teams) from initial triage to final resolution.
- Stakeholder SLA Management: Provide regular, clear, and proactive status updates to internal business teams, client analysts, and external partners during operational outages or data delay incidents.
- Root Cause Analysis (RCA) Communication: Draft clear, client-friendly summary reports explaining data issues, business impacts, and remedial actions taken.
3. Access Management & user support
- Execute user and service account access requests across Snowflake databases, schemas, and dbt models adhering to Role-Based Access Control (RBAC) guidelines.
- Troubleshoot client access issues (e.g., missing database grants, functional role mismatches, or row-access/masking policy restrictions) without compromising data security.
4. Compute Cost & Warehouse Safeguards
- Monitor active Snowflake Virtual Warehouses and identify long-running queries, stuck sessions, or un-suspended resources.
- Safeguard credits by terminating rogue client queries or runaway build tasks and reporting compute spikes to management.
5. Basic Query Debugging & Escalation
- Inspect failed or lagging queries using Snowflake’s Query Profile to identify basic performance bottlenecks (e.g., heavy spilling, massive table scans).
- Perform basic query fixes, adjustments to warehouse sizing, or manual data re-executes.
- Escalate structural dbt code issues, missing indexes/cluster keys, or schema changes to L3 Data Engineers with clear context and logs.
Required Qualifications & Technical Skills
- Client-Facing & Communication: Excellent written and verbal communication skills; comfortable explaining technical data issues to non-technical business stakeholders.
- Ticketing Systems: Hands-on experience working with ticketing tools (e.g., Jira, ServiceNow, Zendesk) and managing operational SLAs.
- Snowflake Experience: 2+ years navigating Snowflake (Snowsight, Query History, Account Usage views, Role/Grant administration, Warehouse monitoring).
- dbt Core Experience: Knowledge of dbt CLI commands , dbt errors, and multi-project dependency handling.
- SQL & Troubleshooting: Solid SQL skills (CTEs, JOINs, window functions, aggregation debugging) to inspect data discrepancies and build failure points.
- Orchestration Familiarity: Basic experience with tools like Airflow, Snowflake Tasks, Dragster or GitHub Actions,.
Key Performance Indicators (KPIs)
- First Response & Resolution SLA: Time to acknowledge and resolve incoming client tickets within agreed timelines.
- ETL Uptime & Data Freshness: Percentage of morning build jobs completed before business client start times.
- Client Satisfaction (CSAT): Positive feedback on ticket communication, transparency, and problem resolution clarity.
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